from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


A						=			logging.get_logger(__name__)


if is_torch_available():
				import torch
				from torch import nn
				from torch.utils.data import Dataset

				from transformers import Trainer





				class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


						'''simple docstring'''






						def __init__(       self     :     str     ,  snake_case__     :     int = 1_0_1		)      ->	List[str]:
								_lowerCamelCase				   =  length
						def __len__(       self     :     str		)      ->	Union[str, Any]:
								return self.length



						def __getitem__(       self     :     List[str]     ,  snake_case__     :     Union[str, Any]		)      ->	int:
								return i





				class     lowerCAmelCase__     :


						'''simple docstring'''






						def __call__(       self     :     Union[str, Any]     ,  snake_case__     :     Tuple		)      ->	Union[str, Any]:
								return {"input_ids": torch.tensor(snake_case__		), "labels": torch.tensor(snake_case__		)}





				class     lowerCAmelCase__     (				nn.Module			):


						'''simple docstring'''






						def __init__(       self     :     Union[str, Any]		)      ->	Any:
								super().__init__()
								# Add some (unused) params otherwise DDP will complain.
								_lowerCamelCase				   =  nn.Linear(1_2_0     ,  8_0		)



						def   _snake_case       (       self     :     int     ,  snake_case__     :     str     ,  snake_case__     :     Optional[int]=None		)      ->	str:
								if labels is not None:
										return torch.tensor(0.0     ,  device=input_ids.device		), input_ids
								else:
										return input_ids





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		@require_torch_neuroncore
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Tuple:
				_lowerCamelCase				   =  f"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
				_lowerCamelCase				   =  self.get_auto_remove_tmp_dir()
				_lowerCamelCase				   =  f"""--output_dir {output_dir}""".split()
				_lowerCamelCase				   =  ['torchrun'] + distributed_args + args
				execute_subprocess_async(snake_case__     ,  env=self.get_env()		)
				# successful return here == success - any errors would have caused an error in the sub-call





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		@require_torch_multi_gpu
		def   _snake_case       (       self     :     Optional[Any]		)      ->	List[Any]:
				_lowerCamelCase				   =  f"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
				_lowerCamelCase				   =  self.get_auto_remove_tmp_dir()
				_lowerCamelCase				   =  f"""--output_dir {output_dir}""".split()
				_lowerCamelCase				   =  ['torchrun'] + distributed_args + args
				execute_subprocess_async(snake_case__     ,  env=self.get_env()		)
				# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
				# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
				#
				# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

				A						=			HfArgumentParser((TrainingArguments,))
				A						=			parser.parse_args_into_dataclasses()[0]

				logger.warning(
				    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
				    F'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
				)

				# Essentially, what we want to verify in the distributed case is that we get all samples back,
				# in the right order. (this is crucial for prediction for instance)
				for dataset_length in [1_0_1, 4_0, 7]:
								A						=			DummyDataset(dataset_length)


								def 	lowerCamelCase					(	UpperCamelCase     :				EvalPrediction							)   ->    Dict:
										_lowerCamelCase				   =  list(range(len(UpperCamelCase							)							)							)
										_lowerCamelCase				   =  p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
										if not success and training_args.local_rank == 0:
												logger.warning(
												    'Predictions and/or labels do not match expected results:\n  - predictions: '
												    F"""{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"""							)
										return {"success": success}

								A						=			Trainer(
								    model=DummyModel(),
								    args=training_args,
								    data_collator=DummyDataCollator(),
								    eval_dataset=dataset,
								    compute_metrics=compute_metrics,
								)
								A						=			trainer.evaluate()
								logger.info(metrics)
								if metrics["eval_success"] is not True:
												logger.error(metrics)
												exit(1)

								A						=			trainer.predict(dataset)
								logger.info(p.metrics)
								if p.metrics["test_success"] is not True:
												logger.error(p.metrics)
												exit(1)

								A						=			2

								A						=			trainer.evaluate()
								logger.info(metrics)
								if metrics["eval_success"] is not True:
												logger.error(metrics)
												exit(1)

								A						=			trainer.predict(dataset)
								logger.info(p.metrics)
								if p.metrics["test_success"] is not True:
												logger.error(p.metrics)
												exit(1)

								A						=			None
def 	lowerCamelCase					(	UpperCamelCase     :				int							)   ->    bool:
		_lowerCamelCase				   =  (1 + 24 * n) ** 0.5
		return ((1 + root) / 6) % 1 == 0


def 	lowerCamelCase					(	UpperCamelCase     :				int = 50_00							)   ->    int:
		_lowerCamelCase				   =  [(i * (3 * i - 1)) // 2 for i in range(1			,       UpperCamelCase							)]
		for i, pentagonal_i in enumerate(UpperCamelCase							):
				for j in range(UpperCamelCase			,       len(UpperCamelCase							)							):
						_lowerCamelCase				   =  pentagonal_nums[j]
						_lowerCamelCase				   =  pentagonal_i + pentagonal_j
						_lowerCamelCase				   =  pentagonal_j - pentagonal_i
						if is_pentagonal(UpperCamelCase							) and is_pentagonal(UpperCamelCase							):
								return b

		return -1


if __name__ == "__main__":
				print(F'''{solution() = }''')